International Business Machines Corporation

Merative US L.p.

Yoonyoung Park

Uri Kartoun

Kenney Ng

Fang Lu

Paul C Tang

Amy Chiu

Charalambos Stavropoulos

Amarendra Das

Amar Das

Olivia Choudhury

Sophie Batchelder

Francis Campion

Issa Sylla

Tanya Rudakevych

Panagiotis Karampourniotis

David Osofsky

Veronica Aldous

Michael J LaScaleia

Melissa Honour

Hillol Sarker

A plurality of events are extracted from a plurality of electronic health records associated with a first patient. The extracted plurality of events are analyzed to identify a plurality of stimulus events and a plurality of response events. An association between a first stimulus event and a first response event is determined. A stimulus-response (SR) variable is generated for the first patient based at least in part on the determined association, and the generated SR variable is integrated into one or more predictive cognitive models.

Identifying and extracting stimulus-response variables from electronic health records

A plurality of attributes are extracted from a plurality of electronic health records, where each electronic health record is associated with a patient in a plurality of patients. Additionally, a training data set and a scoring data set are generated based on the plurality of attributes, and a patient similarity model is trained based on the training data set. A precision cohort is identified, where the precision cohort includes patients in the plurality of patients from the scoring data set that are similar to a first patient based on an electronic health record of the first patient and the similarity model. At least one result statistic for each of a plurality of treatments given to patients in the precision cohort is determined, and a first treatment of the plurality of treatments is selected for the first patient based at least in part on the determined result statistics.

Generating personalized treatment options using precision cohorts and data driven models

Systems, computer-implemented methods, and computer program products that can facilitate anonymizing a network based on factors including network attributes, node attributes, and edge attributes describing connections between nodes are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an anonymizing component that can anonymize network information of the network based on a network attribute for a network and a node attribute of a first node of the network, resulting in an anonymized network.

Anonymizing a network using network attributes and entity based access rights

Embodiments provide a system and method for a customized alert generation for medication adherence. Medication intake instructions and user data are collected and analyzed to determine an availability model of the user to take the medication. A suggested arrangement for the user to take the medication based on the availability model is determined, and the user is alerted according to the suggested arrangement. The user provides feedback on whether the medication was taken upon receiving the alert, and the feedback trains the availability model to provide an improved suggested arrangement for taking the medication.

Medication adherence through personalized alerts

A verification device is configurable for verifying the identity of a person. The verification device may be in communication with a user device and an entity device that may request an authorization decision from the verification device. The verification device may have a collection system configured to populate a voice profile database with a plurality of voice profiles in the form of audio recordings of people. The verification device may also have a testing system configured to select one of the voice profiles, provide the voice profile to an end-user device, and receive an answer from the end-user device, the answer being an attempt by the person to identify an individual associated with the voice profile. The verification device may further have an authentication system configured to determine whether the identity of the person is verified based on the answer from the end-user device.

Identity verification system using voice recognition by an individual

A study design guide method, system, and computer program product, include specifying a search parameter, analyzing a collection of unstructured documents using the search parameter, creating a list of informative terms and informative phrases related to design of a study cohort, and creating a visualization reflecting a level of importance, a frequency, and a relevancy of the informative terms and the informative phrases in relation to each other for a study design using the study cohort.

Utilizing unstructured literature and web data to guide study design in healthcare databases

A system and associated method for assessing level of completeness of healthcare data storage based on collected data. The system includes a collection system, a measurement system, an expectation system, and an alerting system. In a method of assessing the health of stored data, the system collects real-time data from at least one data source, determines measurements for a plurality of parameters based on the collected data, and generates expectations for a future period of time for the plurality of parameters based on data analysis technique. The system also compares the expectations for the future period of time to subsequent measurements collected for that period of time to determine whether the subsequent measurements satisfy an expectation threshold and provide an alert to a client terminal. The alert is a result of the comparison of the expectations and the subsequent measurements and provides an assessment of data storage quality and alerts on anticipated deficiencies.

Systems and methods for determining data storage health and alerting to breakdowns in data collection

Mechanisms are provided to implement a cognitive medical decision support system that operates to analyze a set of commercial transactions executed by a patient to identify at least one of products or services purchased during each commercial transaction or an activity, associated with the commercial transaction, engaged in by the patient. The cognitive medical decision support system determines a lifestyle behavior pattern of the patient based on the products, services, or activities associated with the commercial transactions in the set of commercial transactions and evaluates an impact of the lifestyle behavior pattern to at least one of the overall health of the patient, a specific medical condition of the patient, or a specific previously prescribed treatment of the patient. The cognitive medical decision support system outputs a notification indicating the impact of the lifestyle behavior pattern.

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Cambridge, MA, United States of America

Yoonyoung Park